release 0.9.3

LICENSE

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+MIT License
+
+Copyright (c) 2020 Noah Arc Lab / Huawei
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MANIFEST.in

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+#dispatch files to site-packages
+recursive-include vega *
+recursive-include roma *

README.cn.md

@@ -1,4 +1,4 @@
-# Vega 即将推出
+# Vega
 
 [English](./README.md)
 
@@ -76,3 +76,4 @@ Vega框架组件解耦，并采用注册机制来组合各个功能组件，便
 ## 合作和贡献
 
 欢迎大家使用Vega，有任何疑问、求助、修改bug、贡献算法、完善文档，请在社区提交issue，我们会及时回复沟通交流。
+欢迎大家加入我们的QQ群: 833345709。

README.md

@@ -1,4 +1,4 @@
-# Vega is coming soon
+# Vega
 
 [中文](./README.cn.md)
 
@@ -77,4 +77,5 @@ Of course, this document cannot solve all the problems. If you have any question
 
 ## Cooperation and contribution
 
-Welcome to use Vega. If you have any questions, ask for help, fix bugs, contribute algorithms, or improve documents, submit the issue in the community. We will reply to and communicate with you in a timely manner. We hope that with our joint efforts, we can improve Vega to be a real productivity tool.
+Welcome to use Vega. If you have any questions, ask for help, fix bugs, contribute algorithms, or improve documents, submit the issue in the community. We will reply to and communicate with you in a timely manner.
+Welcome to join our QQ chatroom (Chinese): 833345709.

benchmark/algs/adelaide_ea.yml

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+general:
+    worker:
+        gpus_per_job: 1
+
+pipeline: [random1, mutate1, full1]
+
+random1:
+    pipe_step:
+        type: NasPipeStep
+
+    dataset:
+        type: Cityscapes
+        common:
+            root_dir: /cache/datasets/VOC2012
+        train:
+            list_file: train.txt
+            batch_size: 16
+            num_workers: 4
+            shuffle: true
+            Rescale:
+                size: 512
+            RandomMirror: true
+            RandomColor:
+                color_factor: [0.95, 1.05]
+                contrast_factor: [0.95, 1.05]
+                brightness_factor: [0.95, 1.05]
+            RandomGaussianBlur:
+                kernel_size: 3
+            RandomRotation:
+                rotation_factor: [-10, 10]
+                border_value: [0.485, 0.456, 0.406]
+                fill_label: 255
+            Normalization:
+                mean: [0.485, 0.456, 0.406]
+                std: [0.229, 0.224, 0.225]
+        test:
+            list_file: val.txt
+            Rescale:
+                size: 512
+            Normalization:
+                mean: [0.485, 0.456, 0.406]
+                std: [0.229, 0.224, 0.225]
+
+    search_space:
+        type: SearchSpace
+        modules: ['custom']
+        custom:
+            name: AdelaideFastNAS
+            backbone_load_path: /cache/models/mobilenet_v2-b0353104.pth
+            backbone_out_sizes: [24, 32, 96, 320]
+            op_names: [conv1x1, conv3x3, sep_conv_3x3, sep_conv_5x5, conv3x3_dil3, sep_conv_3x3_dil3, sep_conv_5x5_dil6]
+            agg_size: 64
+            aux_cell: false
+            sep_repeats: 1
+            agg_concat: true
+            num_classes: 21
+
+    search_algorithm:
+        type: AdelaideRandom
+        codec: AdelaideCodec
+        max_sample: 100
+
+    trainer:
+        type: Trainer
+        callbacks: AdelaideEATrainerCallback
+        codec: AdelaideCodec
+        seed: 0
+        epochs: 25
+        print_step: 50
+        optim:
+            type: Adam
+            lr: !!float 5e-5
+        lr_scheduler:
+            type: StepLR
+            step_size: 5
+            gamma: 0.5
+        loss:
+            type: CrossEntropyLoss
+            ignore_index: 255
+        metric:
+            type: IoUMetric
+            num_class: 21
+
+mutate1:
+    pipe_step:
+        type: NasPipeStep
+
+    dataset:
+        ref: random1.dataset
+
+    search_space:
+            ref: random1.search_space
+
+    search_algorithm:
+        type: AdelaideMutate
+        codec: AdelaideCodec
+        max_sample: 100
+        pareto_front_file: "{local_base_path}/output/random1/pareto_front.csv"
+        random_file: "{local_base_path}/output/random1/random.csv"
+
+    trainer:
+        ref: random1.trainer
+        callbacks: AdelaideEATrainerCallback
+        codec: AdelaideCodec
+        save_model_desc: True
+
+full1:
+    pipe_step:
+        type: FullyTrainPipeStep
+        models_folder: "{local_base_path}/output/mutate1/"
+
+    dataset:
+        ref: random1.dataset
+        train:
+            batch_size: 24
+
+    search_space:
+        ref: random1.search_space
+        custom:
+            sep_repeats: 2
+
+    trainer:
+        ref: random1.trainer
+        callbacks: AdelaideEATrainerCallback
+        codec: AdelaideCodec
+        model_statistics: True
+        pareto_front_file: "{local_base_path}/output/mutate1/pareto_front.csv"
+        random_file: "{local_base_path}/output/random1/random.csv"
+        epochs: 100

benchmark/algs/bohb.yml

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+general:
+    worker:
+        timeout: 1000
+        gpus_per_job: 1
+
+pipeline: [hpo1, fully_train, benchmark_cifar10]
+
+hpo1:
+    pipe_step:
+        type: HpoPipeStep
+
+    dataset:
+        type: Cifar10
+        common:
+            data_path: /cache/datasets/cifar10/
+        test:
+            batch_size: 256
+    hpo:
+        type: BohbHpo
+        policy:
+            total_epochs: 81
+            config_count: 242
+            repeat_times: 2
+        pareto:
+            object_count: 2
+            max_object_ids: [0,1]
+        hyperparameter_space:
+            hyperparameters:
+                - key: dataset.batch_size
+                  type: INT_CAT
+                  range: [64, 128, 256]
+                - key: trainer.optim.lr
+                  type: FLOAT_CAT
+                  range: [0.001, 0.003, 0.007, 0.01, 0.03, 0.07, 0.1]
+                - key: trainer.optim.type
+                  type: STRING
+                  range: ['Adam', 'SGD']
+                - key: trainer.optim.momentum
+                  type: FLOAT
+                  range: [0.8, 0.99]
+            condition:
+                - key: condition_for_sgd_momentum
+                  child: trainer.optim.momentum
+                  parent: trainer.optim.type
+                  type: EQUAL
+                  range: ["SGD"]
+
+    model:
+        model_desc:
+            modules: ["backbone", "head"]
+            backbone:
+                base_channel: 64
+                downsample: [0, 0, 1, 0, 1, 0, 1, 0]
+                base_depth: 18
+                doublechannel: [0, 0, 1, 0, 1, 0, 1, 0]
+                name: ResNetVariant
+            head:
+                num_classes: 10
+                name: LinearClassificationHead
+                base_channel: 512
+
+    trainer:
+        type: Trainer
+        epochs: 1
+        optim:
+            type: Adam
+            lr: 0.1
+        lr_scheduler:
+            type: MultiStepLR
+            milestones: [75, 150]
+            gamma: 0.5
+        metric:
+            type: accuracy
+        loss:
+            type: CrossEntropyLoss
+
+    evaluator:
+        type: Evaluator
+        gpu_evaluator:
+            type: GpuEvaluator
+
+fully_train:
+    pipe_step:
+        type: FullyTrainPipeStep
+    dataset:
+        ref: hpo1.dataset
+    model:
+        ref: hpo1.model
+    trainer:
+        ref: hpo1.trainer
+        load_checkpoint: True
+        epochs: 200
+        hps_file: "{local_base_path}/output/hpo1/best_hps.json"
+
+benchmark_cifar10:
+    pipe_step:
+        type: BenchmarkPipeStep
+        models_folder: "{local_base_path}/output/fully_train/"
+    dataset:
+        type: Cifar10
+    evaluator:
+        type: Evaluator
+        gpu_evaluator:
+            type: GpuEvaluator

benchmark/algs/boss.yml

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+general:
+    worker:
+        timeout: 1000
+        gpus_per_job: 1
+
+pipeline: [hpo1, fully_train, benchmark_cifar10]
+
+hpo1:
+    pipe_step:
+        type: HpoPipeStep
+
+    dataset:
+        type: Cifar10
+        common:
+            data_path: /cache/datasets/cifar10/
+        test:
+            batch_size: 256
+    hpo:
+        type: BossHpo
+        policy:
+            total_epochs: 81
+            config_count: 242
+            repeat_times: 2
+        pareto:
+            object_count: 2
+            max_object_ids: [0,1]
+        hyperparameter_space:
+            hyperparameters:
+                - key: dataset.batch_size
+                  type: INT_CAT
+                  range: [64, 128, 256]
+                - key: trainer.optim.lr
+                  type: FLOAT_CAT
+                  range: [0.001, 0.003, 0.007, 0.01, 0.03, 0.07, 0.1]
+                - key: trainer.optim.type
+                  type: STRING
+                  range: ['Adam', 'SGD']
+                - key: trainer.optim.momentum
+                  type: FLOAT
+                  range: [0.8, 0.99]
+            condition:
+                - key: condition_for_sgd_momentum
+                  child: trainer.optim.momentum
+                  parent: trainer.optim.type
+                  type: EQUAL
+                  range: ["SGD"]
+
+    model:
+        model_desc:
+            modules: ["backbone", "head"]
+            backbone:
+                base_channel: 64
+                downsample: [0, 0, 1, 0, 1, 0, 1, 0]
+                base_depth: 18
+                doublechannel: [0, 0, 1, 0, 1, 0, 1, 0]
+                name: ResNetVariant
+            head:
+                num_classes: 10
+                name: LinearClassificationHead
+                base_channel: 512
+
+    trainer:
+        type: Trainer
+        epochs: 1
+        optim:
+            type: Adam
+            lr: 0.1
+        lr_scheduler:
+            type: MultiStepLR
+            milestones: [75, 150]
+            gamma: 0.5
+        metric:
+            type: accuracy
+        loss:
+            type: CrossEntropyLoss
+
+    evaluator:
+        type: Evaluator
+        gpu_evaluator:
+            type: GpuEvaluator
+
+fully_train:
+    pipe_step:
+        type: FullyTrainPipeStep
+    dataset:
+        ref: hpo1.dataset
+    model:
+        ref: hpo1.model
+    trainer:
+        ref: hpo1.trainer
+        load_checkpoint: True
+        epochs: 200
+        hps_file: "{local_base_path}/output/hpo1/best_hps.json"
+
+benchmark_cifar10:
+    pipe_step:
+        type: BenchmarkPipeStep
+        models_folder: "{local_base_path}/output/fully_train/"
+    dataset:
+        type: Cifar10
+    evaluator:
+        type: Evaluator
+        gpu_evaluator:
+            type: GpuEvaluator